SPA-MAE adapts an MAE backbone with a physical prior module providing parameter-aware and structure-aware guidance to pretrain on CSI data, yielding better downstream performance than prior CSI foundation models with fewer parameters.
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Large wireless model (LWM): A foundation model for wireless channels
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UNVERDICTED 10representative citing papers
SiFo pretrains a CSI feedback model on source sites and uses RSRP-based user matching to calibration memory for site-specific subspace guidance at target sites without parameter updates.
Channel intrinsic dimensionality dNL (5-35) sets the scaling ceiling for wireless foundation models, with diminishing returns past ~30M parameters and pilot-aided test-time training on 12M models beating 96M static models by 7-10 dB.
RFPrompt adapts the Large Wireless Model via deep prompt tokens to improve out-of-distribution robustness in modulation classification while training only a small number of parameters.
WiFo-MiSAC is a task-agnostic foundation model that unifies multimodal wireless signals via tokenization and self-supervised learning with SS-DMoE to achieve strong few-shot performance on beam prediction and channel estimation.
A pre-trained interference-aware graph Transformer model for wireless resource allocation that achieves strong few-shot adaptation to new tasks and scenarios.
WiFo-2 is a space-time-frequency foundation model pretrained on heterogeneous CSI data that delivers strong zero-shot and few-shot performance across wireless communications and sensing tasks.
Wireless data lacks the self-contained tokenized substrate of text, so monolithic wireless world models are unsuitable for 6G; composable agentic systems using specialized components and explicit interfaces are the realistic alternative.
A two-stage reinforcement learning system on pretrained LLMs aligns channel state information with user intents to generate adaptive, physically realizable link construction strategies for 6G that outperform conventional methods in experiments.
Enwar 3.0 is an LLM-orchestrated framework that uses a sensor degradation classifier and context-aware agent coordination to achieve over 88% beam selection accuracy, 98% blockage F1-score, and 87% reasoning correctness in mmWave vehicular networks.
citing papers explorer
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SPA-MAE: A Physics-Guided CSI Foundation Model for Wireless Physical Layer
SPA-MAE adapts an MAE backbone with a physical prior module providing parameter-aware and structure-aware guidance to pretrain on CSI data, yielding better downstream performance than prior CSI foundation models with fewer parameters.
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SiFo: Wireless Foundation Model for Low-Overhead Site-Specific CSI Feedback
SiFo pretrains a CSI feedback model on source sites and uses RSRP-based user matching to calibration memory for site-specific subspace guidance at target sites without parameter updates.
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How Big Should a Wireless Foundation Model Be?
Channel intrinsic dimensionality dNL (5-35) sets the scaling ceiling for wireless foundation models, with diminishing returns past ~30M parameters and pilot-aided test-time training on 12M models beating 96M static models by 7-10 dB.
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RFPrompt: Prompt-Based Expert Adaptation of the Large Wireless Model for Modulation Classification
RFPrompt adapts the Large Wireless Model via deep prompt tokens to improve out-of-distribution robustness in modulation classification while training only a small number of parameters.
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WiFo-MiSAC: A Wireless Foundation Model for Multimodal Sensing and Communication Integration via Synesthesia of Machines (SoM)
WiFo-MiSAC is a task-agnostic foundation model that unifies multimodal wireless signals via tokenization and self-supervised learning with SS-DMoE to achieve strong few-shot performance on beam prediction and channel estimation.
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A Graph Foundation Model for Wireless Resource Allocation
A pre-trained interference-aware graph Transformer model for wireless resource allocation that achieves strong few-shot adaptation to new tasks and scenarios.
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WiFo-2: a generalist foundation model unifies heterogeneous wireless system design
WiFo-2 is a space-time-frequency foundation model pretrained on heterogeneous CSI data that delivers strong zero-shot and few-shot performance across wireless communications and sensing tasks.
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Against the Monolithic Wireless World Model: Why NextG Needs Composable and Agentic Intelligence
Wireless data lacks the self-contained tokenized substrate of text, so monolithic wireless world models are unsuitable for 6G; composable agentic systems using specialized components and explicit interfaces are the realistic alternative.
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Agentic Link Construction for Environment and Intent Aware 6G Communication
A two-stage reinforcement learning system on pretrained LLMs aligns channel state information with user intents to generate adaptive, physically realizable link construction strategies for 6G that outperform conventional methods in experiments.
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Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Management
Enwar 3.0 is an LLM-orchestrated framework that uses a sensor degradation classifier and context-aware agent coordination to achieve over 88% beam selection accuracy, 98% blockage F1-score, and 87% reasoning correctness in mmWave vehicular networks.